Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2007- 2008
Statistical Modeling

Data Science

Mathematical Analysis andStatistical Inference

Prediction andKnowledge Discovery

Risk Analysis

Engineering andTechnical Support

CONTENTS
Message from Director-General

1

Organization of the Institute2

Introduction to Our Research

4Strategic Research

5

ISM Research Projects

12

Research Cooperation
International Cooperation

19

Research Collaboration

19

Graduate School Program

21

Outreach Activities
Tutorial Courses

24

ISM Seminar

25

Special Lecture

25

Statistical Consultation Service

26

Annual Research Report Meeting for 2006

26

Finance and Buildings

27

Facilities and Equipment

28

Organization

29

History33

Message from Director-General
Message from Director-General

Remarkable advances in information and communication technology,
including measuring, telecommunication, information retrieval, andcomputation technologies, have changed people’s lifestyles substantially.
It is no exaggeration to say that people can no longer live without information from the Internet. Ironically, it is becoming increasingly difficult tofind the essential information from data. The situation is much the samein the field of academic research. The emergence of large scale data anda change in research style have brought about many difficult problems
that cannot be solved by traditional methods. However, facing withmany challenges like this, we rather think them as good opportunities forinnovating statistical science, since new methods of statistical science,such as the one for knowledge acquisition from data, have been developed
corresponding to changes in data environment and emergence of new areasof scientific research.
Foreseeing such a new era, the Institute of Statistical Mathematics established the Prediction and Knowledge Discovery Research Center in 2003.
With the aim of inventing and applying methods of prediction and knowledge
discovery based on large scale data, this center focuses on four research
projects: Data Assimilation, Statistical Seismology, Molecular Evolution
and Genome Diversity. In 2005, the Risk Analysis Research Center was
set up to establish Quantitative Risk Science as a base for scientific risk
management. Three research groups, namely, the Food and Drug Safety
Research Group, Environmental Risk Research Group and Financial Risk
and Insurance Research Group, are engaged in study at the center. In 2004,
when our Institute became a corporate body, it formed the Research Organization of Information and Systems together with National Institute ofInformatics, National Institute of Genetics and National Institute of Polar
Research, and started relevant activities. In particular, as an activity of
the Transdisciplinary Research Integration Center, a new research centercreated within this organization, we actively promote the Function andInduction Research Project in an effort to construct new scientific inference
models suitable for the information age.
The goal of the Institute of Statistical Mathematics as an Inter-University
Research Organization is to perform fundamental and practical research
to address the real-world problems and needs of the information age,while advancing joint research projects with researchers in other fields.
Moreover, to provide more systematic graduate education on statisticalscience, the Department of Statistical Science of the Graduate University
for Advanced Studies, for which our Institute serves as the parent organization, began offering a five-year doctoral educational program startingwith a graduate course, in academic year 2006. The Institute is also awareof growing concern over the lack of initiatives to promote mathematics andmathematical science in Japan and the need to take measures to foster
such initiatives. As a research institute engaging in the development offundamental methodology, we hope to actively contribute to the advancement of the mathematical science in Japan.
To achieve these goals, I would like to ask for your continuous support
for the Institute of Statistical Mathematics.

Genshiro Kitagawa
Director-General
The Institute of Statistical Mathematics

1

Organization of the Institute

Basic Research
Department of Statistical Modeling
Organization of the Institute

The Department of Statistical Modeling conducts research on the modeling of causally, temporally and/or
spatially interrelated complex phenomena, including intelligent information processing systems. It also conducts
on model-based statistical inference methodologies.

䡵 Spatial and Time Series Modeling GroupThe Spatial and Time Series Modeling Group works on modeling and inference for the statistical analysis oftime series, spatial and space-time data, and their applications to prediction and control.

䡵 Intelligent Information Processing GroupThe Intelligent Information Processing Group works on concepts and methods for the extraction, processing
and transformation of information in intelligent systems, motivated by an active interest in practical problems inengineering and science.

䡵 Graph Modeling GroupThe Graph Modeling Group works on analyses of the data generated by systems with a graph structure and on
the modeling required in order to reconstruct the original system.

Department of Data Science
The Department of Data Science aims to develop research methods for surveys, multidimensional data
analyses, and computational statistics.

䡵 Survey Research GroupThe Survey Research Group focuses on research related to statistical data collection and data analyses.

䡵 Multidimensional Data Analysis Group
The Multidimensional Data Analysis Group studies methods for analyzing phenomena grasped on multidimensional space and ways for collecting multidimensional data.

䡵 Computational Statistics GroupThe Computational Statistics Group studies sophisticated uses of computers in statistical methodology suchas computer-intensive data analyses, computational scientific methods and statistical systems.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statistical theory, statistical learning theory, the theory of optimization, and the practice of statistics in science.

䡵 Mathematical Statistics Group
The Mathematical Statistics Group is concerned with aspects of statistical theory and probability theory thathave statistical applications.

䡵 Learning and Inference Group
The Mathematical Statistics Group develops statistical methodologies that enable researchers to learn fromdata sets and to properly extract information through appropriate inference procedures.

䡵 Computational Mathematics GroupThe Computational Mathematics Group studies computational algorithms together with mathematical methodologies used for statistical modeling in the sciences.

2

Strategic ResearchPrediction and Knowledge Discovery Research Center
Organization of the Institute

The Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference algorithms
that can be used to extract useful information from the huge amount of data which complex systems produce, and thus
attempts to solve real-world problems in many different scientific domains, especially genomics, earth and space sciences.

䡵 Molecular Evolution Research Group
The Molecular Evolution Research Group researches the area of molecular phylogenetics, and seeks to develop
statistical methods for inferring evolutionary trees of life using DNA and protein sequences.

䡵 Data Assimilation Research GroupThe Data Assimilation Research Group aims at developing new, advanced data assimilation techniques tocombine different information from dynamical simulation and observation data.

䡵 Statistical Seismology Research GroupThe Statistical Seismology Research Group is concerned with the evaluation of seismicity anomalies, detectionof crustal stress changes, their modeling, and the probability forecasting of large aftershocks and earthquakes.

䡵 Statistical Genome Diversity Research GroupThe Statistical Genome Diversity Research Group aims to construct novel methodologies for learning and inference
from a variety of data sets in the rapidly growing area of bioinformatics.

Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the study of the increased uncertainty
and risk associated with the increasing globalization of society and the economy. The center is also constructing a network for risk analysis in order to contribute to the creation of a reliable and safe society.

䡵 Food and Drug Safety Research GroupThe Food and Drug Safety Research Group aims to develop the statistical framework and methodology ofquantitative risk evaluation for substances ingested by the human body.

䡵 Environmental Risk Research GroupThe Environmental Risk Research Group studies the statistical methodologies related to environmental risk andenvironmental monitoring.

䡵 Financial Risk and Insurance Research GroupThe Financial Risk and Insurance Research Group explores the use of statistical modeling methods to quantify
the risks involved with financial instruments and insurance products.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

䡵 Computing Facility Unit

The Computing Facility Unit is in charge of the management of computer facilities andsoftware for research.

䡵 Networking Facility Unit

The Networking Facility Unit is in charge of the management of networking infrastructureused for research and is responsible for network security.

䡵 Education and Library Unit

The Education and Library Unit is in charge of planning statistical education courses topopularize research results and is responsible for maintaining an extensive library.

䡵 Public Outreach Unit

The Public Outreach Unit is in charge of the publication and editing of research results
and is responsible for public relations.

3Introduction to Our Research

Strategic Research
The ISM is divided into 3 categories; Basic Research, Research Support, and Strategic Research. The Basic
Research section provides a framework for fundamental research, accepting original contributions from all
staff members, with an emphasis on developing statistical methods that can address current societal issues.
Research Support acts precisely as its name implies, as a part of the Inter-University Research Organization.
Strategic Research also combines the strengths of Basic Research, Research Support, and other universities
and research institutes, in order to address important societal issues.
The Strategic Research section has established the Prediction and Knowledge Discovery Research Centerand the Risk Analysis Research Center. These two centers are organized as follows:

Prediction and Knowledge Discovery
Research Center ( PKDRC )Molecular Evolution Research Group
Data Assimilation Research Group

Introduction to Our Research

Statistical Seismology Research Group
Statistical Genome Diversity Research Group

Risk Analysis Research Center ( RARC )Food and Drug Safety Research Group
Environmental Risk Research Group
Financial Risk and Insurance Research GroupISM Research Projects
The institute also emphasizes the importance of its own and the exploratory research. Every year we
invite researchers from within the institute to propose new research projects. A relatively sizableamount of funding is allocated to approved projects, and strategic research leading to future development
is encouraged. In 2006, we have approved the following ten projects. No limitation was placed on theme
or category.
Research Theme

Project Leader

Reconstructing functional neuronal circuits from optical imaging data

Yoshiyasu Tamura

Computational geometry and statistical distribution of symmetry

Yoshiaki ItohEstimation of quantum states from observation

Yukito Iba

The estimation of space- and time-spectrum in dispersive wave field

Makio Ishiguro

Organizing the international symposium on stochastic models and discrete geometry

Masaharu Tanemura

Developing statistical models for bycatch in tuna fisheries

Mihoko Minami

Analytic center of the system of linear matrix inequalities and its applications

Takashi Tsuchiya

The research activities of each group and a profile of each research project within the Strategic ResearchCenter are as follows.
4Strategic Research

Molecular Evolution Research Group

Toward Integrative Understanding of Biodiversity
In Madagascar, we are working on the biogeographic problems of tenrecs and lemurs (mammals) andbaobabs (plants). Spiny tenrec is morphologicallyvery similar to hedgehog, and had long beenclassified in Insectivora together with hedgehogs,
moles, and shrews, but molecular phylogenetic
analyses clarified that tenrecs belong to Afrotheria
which includes elephants, hyraxes, and aardvarks,
and that the similarity between spiny tenrec andhedgehog is due to convergent evolution. Estimation of the time-scale of lemurs and baobabs gave
important clues in clarifying the origin of thesegroups of organisms in Madagascar.
Jun Adachi

Introduction to Our Research

The aim of this project is to understand thebiodiversity on Earth from various view points
encompassing from molecular level to ecologicallevel. Evolutionary view is indispensable in the
integrative understanding of the biodiversity, andthe methods for inferring molecular phylogeny
are essentially important. In this project, we aredeveloping models of nucleotide substitutions
during evolution and methods for phylogenetic
inference based on maximum likelihood. In developing the statistical methods, we simultaneously
try to solve real problems of biological importance.
The biological problems we are working on include evolution of vertebrates such as mammals
and birds, evolution of land plants, and the biodiversity of Malagasy fauna. Madagascar has been
isolated from any continents for a long geologicaltime, and has developed a unique fauna with highlevel of combined species richness and endemism.

Lemurs present remarkablediversity in Madagascer

Lemuridae

Cheirogaleidae

Indriidae

Daubentoniidae

5Strategic Research

Data Assimilation Research Group

Data Assimilation : Time-dependent Information Fusion from Numerical
Simulation and Large-scale Observation

Introduction to Our Research

Data Assimilation (DA) is a technique for a synthesis of information from a dynamic (numerical)
model and observation data. It is an emerging area
in earth sciences, particularly oceanography,
stimulated by recent improvements in computational and modeling capabilities and the increasein the amount of available observations. In statistical methodology, DA can be formulated in thegeneralized state space model, where the systemand observation model correspond to large-scale
numerical model-based simulations and largescale satellite- and/or ground-based measurement systems, respectively. Past studies for DA
employed a linear Gaussian state space model andapplied Kalman filter. The Kalman filter based
methods, however, do not allow for the strong
nonlinear and/or non-Gaussian disturbance behaviors. Many phenomena in earth sciences tend to
be discussed in terms of a complex system inwhich the nonlinear non-Gaussian fluctuations (disturbances) play an important role. The nonlinear

non-Gaussian DA method needs to be developedin an attempt to improve a performance of prediction ability of our environment. We are therefore
constructing new computation methods basedon the sequential DA methods and conducting
four DA projects. One is done with the ensemble
Kalman filter that assimilates the TOPEX/Poseidon
altimetry to the coupled ocean-atmosphere simulation model. The second DA is done with a particle
filter for Tsunami simulation model to correctbottom topography. The third is the DA project
to estimate distributions of ring current ions andelectric potential in the inner magnetosphere by
assimilating the series of the ENA data obtainedby the HENA imager on board of IMAGE satellite
into a kinetic ring current model (CRCM). The
last project is to apply the DA methodology to
combine a simulation model with observed datalike microarray gene expression data for understanding biological pathways.
Tomoyuki Higuchi

Figure: Numerical experiment to estimate ring current distribution by DA.

6Introduction to Our Research

Statistical Seismology Research Group

Precursory Seismic and Geodetic Anomalies Detected by Statistical Models
䡵 Motivation behind this research
Seismic quiescence and activation, as the precursors to large earthquakes, have attracted much attention among seismologists. Of particular interest is the
hypothesis that the stress-changes transferred from arupture or silent slip in one region can cause seismicchanges in other regions. However, the clusteringfeature of earthquakes prevents us from detecting theseismicity change due to stress change transferredfrom other region because successive earthquakes
are triggered by complex mechanisms under heterogeneous fractal media. Nevertheless, we can use thestatistical empirical laws as a practical method forpredicting earthquake clusters. Thus, the objectiveof this research is to demonstrate that diagnostic
analysis based on fitting the Epidemic Type Aftershock Sequence (ETAS) model to regional seismicity
can be helpful in detecting exogenous stress changes
there. In particular, the changes due to silent slips
are usually so slight that one can barely recognize
systematic anomalies in seismicity without the aid of
the ETAS model.

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䡵 Residual analysis of geodetic time series
records from the GPS network
Such slip is further supported by transient crustal
movement around the rupture source. The time series
records of many baseline distances between thepermanent GPS stations significantly deviated fromthe predicted linear trends, mostly with the deviations
consistent with the coseismic horizontal displacements of the GPS stations due to the Chuetsu Earthquake. Furthermore, a deeper extension of themainshock fault plane, with a lower dip angle, canexplain the more cases of the observed baseline deviations in comparison with the theoretical displacement.
The cumulated precursory slip is estimated to beroughly equivalent to M6 in the moment magnitude.
Yosihiko Ogata

䡵 Diagnostic analysis from earthquake sequence
The ordinary short-term occurrence rate of earthquakes in a region is well predicted using the ETAS
137
39

Introduction to Our Researchmodel of triggering earthquakes. Any anomalous
seismic activity, such as quiescence and activation,
can be quantified by identifying significant deviationfrom the predicted rate. Such anomalies are revealed
to have occurred during the three year period leading
up to the 2004 Chuetsu Earthquake of Magnitude 6.8,
central Japan. The quiescence and activation, which
took place in the region of negative and positive increments of the Coulomb Failure Stress, respectively,
were possibly caused by silent slip on the focal fault
plane, or its deeper extension with a lower dip angle.

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TIME ( YEAR )

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Strategic Research

Statistical Genome Diversity Research Group

Peak Pattern Recognition from Proteome

Introduction to Our Research

Recently a large scale of dataset has been developed and produced from genomic and proteomic
experiments with high enhancements. It enables
us to analyzing experimental subjects that werepreviously impossible and to opening a newparadigm in medical and biological sciences.
The role of statistics is to discover new knowledge based on given datasets and to validate theconfirmation for the discovery. The statisticalrole on learning genomic and proteomic datasets
is not properly established because of the datascales beyond ordinary assumptions. In particular
the characteristic point is high-dimension andsmall sample. For this there are easily exposed

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Figure 1: Plots for 100 TOF mass frequency curves for each class label.

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Figure 2: Plots for occupancy percentage at 30 peaks detected for eachclass label.

8

false discovery and missing discovery, which
dualistically faces to the data understandings.
For attaining the challenging goals in medicine
and biology it is necessary and important to conduct correct knowledge and reasonable decisions
induced from genome datasets. The objective ofour research group aims at proposing statisticalmethods to support and extract sensible knowledge discovery from huge amounts of feature information. In particular we are tackling genome
datasets in a biological circle which would integrate microarray associated with gene expressions,proteome with protein expressions and SNPs
with polymorphism. We challenge to discover
any biomarkers for phenotypes including cancer
diseases, effects of treatments, adverse effects
of drugs and so forth by leaning a combinationof genome datasets and other clinical informationon the basis of cooperative work with Genome
Center of Japan Foundation for Cancer Research.
Our target is to explore only peaks truly associated with the target phenotype in the hull of several thousands of peaks. To the problem our groupproposes an idea of “common peaks” in the paper
by Fushiki, Fujisawa and Eguchi, BMC Bioinformatics (2006) 7:358. We confirmed a reasonable
performance of this prediction method for applying to Ovarian Datasets 8-7-02 that are publicly
available from the National Cancer Institute in
U.S.A.
Furthermore we are presently analyzing data
sets for 65 breast cancer patients in 2003-2004 at
Cancer Institute Hospital and making a predictionof the effect for an anti-cancer drug based on theproteomic data. We found that it needed more
device than the idea of common peak approach.
Hence we improved to assess the common peaks
separately in each group of the same class label.
See Figures 1 and 2. As a result we concluded that
this semi-supervised common peak detection
conducted excellent performance with hit rate
14 over 15 for a test dataset.
Shinto Eguchi

Introduction to Our Research

Food and Drug Safety Research Group

Benefit-risk Balance Evaluation of Food and Drug
䡵 Mission
Social responsibility of the food and drug safety
program in RARC is to design appropriate databases and statistical methods for the benefit-riskbalance evaluation of food and drug. Our researchis based on cooperative research projects with visiting researchers and members from the researchgroup of data management and statistics in Japan
Pharmaceutical Manufacturers Association.

Suicide rate * ( empirical Bayes estimation )Japan, 1998-2000

䡵 Ad hoc study on a specific drug safety issue
A safety issue of drug often becomes a social
concern, and the immediate scientific elucidation
is demanded. An appropriate study design, implementation and statistical analysis are necessary
to get an accurate scientific quantitative answer.
We participate in an ad hoc study on a specificdrug safety issue as experts of statistical scienceand epidemiology.
The figure below shows the geographic featureof the recent sharp increase in suicide deaths
since 1998. This ecological study indicates thatthe suicide death rate correlates negatively withthe amount of SSRIs (Selective Serotonin Reuptake
Inhibitors) prescribed.
Toshiharu Fujita

Introduction to Our Research

䡵 Benefit/risk analysis of drugs based onlarge-scale database
There is no large-scale database available incomplementing the spontaneous reporting system
in Japan at this moment. To conduct studies to generate hypotheses about drug effects, to strengthen
them, and/or to test a priori hypotheses about
drug effect, we are under the process of building
original databases using data collected from premarketing clinical trials and post-marketing
Drug Use Investigation conducted for the Drug
Re-examination application by pharmaceuticalmanufacturers, successively. For example, a casecontrol analysis on the Angiotensin convertingenzyme inhibitor-induced cough was conductedutilizing our post-marketing database of anti-hypertensive drugs.

Suicide rate ratio( 1998-2000 / 1989-1995 )* Per 100,000 population

Mortality rate
( M. R. )50 < M. R.
30 < M. R. <= 40
unknown

40 < M. R. <= 50
20 < M. R. <= 30

Mortality Rate
Ratio

175 < Ratio
125 < Ratio <=150
Ratio <=100150 < Ratio <=175
100 < Ratio <=125
unknown

Geographic clustering of suicide mortality among males aged 10 and older

9Strategic Research

Environmental Risk Research GroupThe Effort of the Statistical Science towards Solution of an Environmental Problem

Introduction to Our Research䡵 Mission
A risk analysis research center / environmental
risk research section aims at performing the contribution towards solution of the environmental
problem which is a modern subject by developing
the optimal new statistical methodology for eachenvironmental problem (for example, the problem
of dioxin, the problem of global warming, the problem of continuous use of safe water). Moreover,in order to realize this purpose, in cooperation
with the community of environmental science,research is carried out including a visiting teacher
or a project researcher.

because there are many unknown sources of dioxins. Accordingly, we are developing statisticalmodels which enable us to make inference on unknown sources. Additionally, to improve the accuracy of the inference, we are trying to complete thedata of dioxins cooperating with National Instituteof Environmental Studies and many environmentallaboratories of local governments.
䡵 Error analysis of Retrieved CO2 column densityfrom the earth observation satellite GOSAT
There are some errors which are contained to
the retrieved CO2 column density from GOSAT(Greenhouse gases Observing SATellite) (Fig.2).
The errors are due to the given condition of thetemperature and water vapor amount, the noises
which are caused by the sensor device, the process from the signal (interferogram) which isobserved to spectrum, and so on. The purposeof this study is to obtain the confidence intervalof the retrieved CO2 column density due to theseerrors. The error models are constructed until
now. Fig.3 is indicated the flow from the spectrum
which is inputted to sensor to the observed
spectrum, and the error factors which are taken
into account in this study.
Koji Kanefuji

䡵 Source identification of dioxins
The toxicity of a certain dioxin(Fig.1) is stronger
than that of the poison gas, sarin, and it is fearedthat dioxins threaten human health. The major
sources of dioxins are activities of human beings
including agricultural chemicals, bleaching andcombustion processes. Dioxins emitted into environment are absorbed into the body of a humanbeing through lungs and a food chain. Recently, theenvironmental contamination caused by dioxins isfrequently detected in individual districts. To solve
the problem, it is necessary to identify the sources.
However, it is not easy to identify the sources,
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polychlorinated dibenzofurs

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dioxin-like polychlorinated biphenyls

Figure 1: Dioxins

fixed mirror
temperaturewater vapor amountspectral radiance(input )( Fourier transform spectrometer )Figure 2: GOSAT image ( photo: JAXA )moving mirror

optical filter
detector

16bit quantization noise
sampling jitter

shot noise
detector noise

AD converter

0 fillinginterferogram
FFT

observed
spectrumelectrical filter

Figure 3: The flow from the spectrum which is inputted to sensor to the observed spectrum, and theerror factors ( bold letters ). This figure makes under discussion with GOSAT Research Team,National Institute for Environmental Studies Center for Global Environmental Research.

10

Figure 4: Ames Salmonella Mutagenicity
Assay( Courtesy by Prof. Takanashi )Introduction to Our Research

Financial Risk and Insurance Research Group

Quantitative Risk Analysis Based on Credit Derivatives
premium to the seller on some regular basis,
while the seller promises to compensate the buyer
for the loss when a credit event occurs. The premium is quoted as a rate of the notional value,called the CDS price. CDS is used to transfer thecredit risk without selling the asset itself, and isa building block for synthetic credit structured
products that match investors’ requirements effectively. Hence, the CDS market is growing so rapidly
in Japan as well as in Europe and the United States.

䡵 Credit Default Swap
A corporate bond goes below par if the bondoffering company causes failure to pay or evengoes into bankruptcy. Bond holders can sustain
economic damage from such a credit event.
Credit Default Swap (CDS) is an insurance contractthat protects the buyer against the loss of thepar value due to a credit event. The buyer pays a䡵 Risk analysis of Japanese CDS market
By analyzing fluctuations of CDS prices, thisresearch project aims to reveal the business environment of an individual company, and of an industrial sector. Finally, we propose the CDS index
that elucidates the Japanese economic ambience.
Figure 1 shows the temporal transition of thehistograms of CDS prices; the price distributions
are skewed to the right with many missing values
and the numbers of observations are extremely
unbalanced among the examined points of time.
A state space modeling with appropriate datatransformations enables us to construct a CDS
index. Figure 2 presents the CDS index with proportions of different credit ratings. The peak inthe middle of the graph shows the big influence
from downgrading GM and Ford in the US. Thisillustrates our method succeeds in capturing theincreased risk due to credit events quantitatively, and at the same time, the primal source ofrisk uprush can be expressed.
Yoshinori Kawasaki

freq.

time
0

d
sprea
CDS

Figure 1: Temporal transition of histograms of CDS prices.
( Source: Bloomberg LP )Introduction to Our Research䡵 Aim of our group
Financial Risk and Insurance Research Group
mainly concerns the theory and practice in thequantitative analysis of risks involved with various
financial instruments and insurance products
from the view point of statistical modeling. Various
research projects are under way not only by ISM
researchers but by visiting and post doctoral researchers. This article introduces an ongoingproject led by Dr. Tanokura, Research Associate.

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CDS INDEX

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10Both figures are provided by Dr. Yoko Tanokura( Research Associate, Risk Analysis Research Center ).

Jul-06

Apr-06

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Oct-05Jul-05

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Jul-04

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Figure 2: Estimated CDS index with proportions of different ratings.

11

ISM Research Projects

Reconstructing Functional Neuronal Circuits from Optical Imaging Data䡵 Introduction
Even when we breathe unconsciously, neurons
give a command to breathe. Neurons do their jobs byconstituting functional neuronal circuits and mutually
exchanging information. Since the last fiscal year, wehave been analyzing neural membrane electric potential image data of rat brainstem related to respiration.

䡵 Re-exploration of basic properties of the data

Introduction to Our Research

For this purpose, correlation analysis has been used
for a limited number of regions, which are known inprevious physiological studies to have functions forrespiration. However, feedback and causality between
regions cannot be extracted by the correlation analysis. Therefore it is needed to develop a new method
for this purpose.
Before developing a new method, we re-exploredbasic properties of the data. In order to investigate
distinctive signals in a specific frequency domain,
power intensity maps based on FFT were investigated.
Then, we found some regions that have relatively
higher intensity around 5 Hz oscillations (Fig.1).
However, they did not always correspond to physiologically meaningful regions; they were even outside oftissue. After investigating equipments for data recording, we recognized that the 5Hz signal is caused byeigen oscillation of the fluorescent microscope (MVX10). Then, equipments were improved and replaced
to a new system and the eigen oscillation was sensibly reduced. In the current status, more reliable data
recording is available.
Figure 1:
Intensity map of neural
membrane electric potential image data on rat
brainstem (around 5Hz)

Figure 2:
Comparison between thepredicted and the measured respiratory neuronal
activities.
( Top) : The measured
value (the solid line) andthe estimated value ( thedoted line) of the respiratory motor output from
C4VR.
(Bottom ) : Time series
data of the pixel, basedon which above “predicted value” was composed.

12䡵 Estimation of the respiratory motor output from themembrane potential imaging data and classificationof the respiratory-related neuronal activities
Two respiratory-related brainstem regions, theparafacial respiratory group and the preBötzinger
complex, have been recently identified by physiological experiments. However, the dynamical interaction
between the two regions and the dynamical process toform the respiratory motor output remain unanswered.
In order to classify the patterns of respiratory-related
neuronal activities, we considered a model by which
the respiratory motor output from the fourth ventral
root of the cervical spinal cord (C4VR) can be predicted using optical recording data. We assumed a simple
model consisting of sigmoid function and [first-orderdelay + dead time] transfer function, and the model
parameters were obtained with a non-linear optimization technique (Fig.2). We found that the characteristics of neuronal activities can be classified by cluster
analysis using these model parameters.

䡵 Mechanism of respiratory rhythm generation
It has not yet shown how respiration rhythm isgenerated. I thought that the rhythm is generated bycommunications among neuron groups that locate
different regions and play different roles, and hence
investigated correlations between various pairs of variables (Fig.3). Each variable has the strongest positive
correlation with the preBötzinger Complex and correlations between every pair were not negative. It implies
that all locations in an observed region tend to moreor less synchronize mutually, and regions that merely
inhibit other regions may not exist. (Data offered by
Hyogo Medical College and Figure 1 offered by Chiba
University)

Seiji Kawai, Fumikazu Miwakeichi, Eiki Tanaka

Figure 3:
Map of correlations between membrane potential of each locationand the respiratory activity (output of a phrenic nerve) at each lag.

Introduction to Our Research

Computational Geometry and Statistical Distribution of Symmetry

Figure 1

try groups. The statistical distribution strongly
depends on the value Z. For the case ANX=ABX3,
if Z=1, the point group Oh is frequent while Z=2
the point group D6h is frequent. The case 8 < Z israre and we show the structure Iron Sulfate (IV)(FeSO3) with Z=18 in Figure 1.
We studied the random sequential packing. The
random sequential packing of caps into a sphere
will help to understand the geometrical structureof diatoms or pollen. The random sequential packing of cubes into torus generates a space group O 7
given by the Figure 2.
Yoshiaki ItohIntroduction to Our Research

What is the most probable structure in nature ?
We introduced a method that defines the species(representatives) of inorganic compounds, andstudied the statistical distribution of the defined
species among space groups, by using ICSD (Inorganic Crystal Structure Database ). ICSD has thedescriptor ‘ANX’ (formula type). This descriptor
is to describe a chemical structure roughly by its
oxidation numbers of the chemical elements (e.g.
ANX=AX describes NaCl, ANX=ABX3 describes
CaCO3). We found that the number Z of formulaunits in a unit cell gives a natural classification tounderstand the statistical distribution of symme-

Figure 2

Members: Michel Deza ( Ecole Normale Supérieure, European Academy of Sciences )Nikolai Dolbilin ( Steklov Mathematical Institute, Moscow State University )Mathieu Dutour ( Rudjer Boscovic Institue )Yoshiaki Itoh, Teruhisa Sugimoto, Masaharu Tanemura

13

ISM Research Projects

Estimation of Quantum States from Observation

Introduction to Our Research䡵 Inference on quantum states
In these fifteen years, various types of “quantum
information technology” arise, such as quantum
computation, quantum cryptography. An art ofthe estimation of the quantum states realized byexperiments is essential in any of these fields. Thisart is sometimes called “quantum tomography”,
because a quantum state is reconstructed from a
set of observations each of which corresponds tothe measurement of a “projection” of the quantum
state. It has a close analogy with CT-scan used in
hospitals.
䡵 Background and aim
The aim of the project is to develop algorithms
for quantum state estimation based on modern
numerical and statistical science. Since reconstruction of hidden states from noisy and incomplete observation is a central problem in modern
statistical science, development of efficientmethods of the estimation of quantum stateswith error assessment will be a great challenge to1statistical scientists. A target of the project is toimprove methods of the estimation of density
matrices, which is a typical example of large-scaleconvex programming, a subject extensively studied by researchers in ISM.
䡵 Current status
We are collaborating with Keiji Matsumoto (NII)and Mituhiro Fukuda (Tokyo Tech) in this project.
We implemented a convex programming algorithm
for the maximum likelihood estimation of density
matrices and successfully test it with sets of artificial data. We are now trying to apply it to the analysis of real experimental data. The figure shows theresult of an numerical experiment with artificial
data. In the figure, upper panels (blue) show theoriginal density matrix (each panel corresponds
to real and imaginary part respectively), andlower panels (pink) show the reconstructed density matrix from a set of artificial data. Accurate
reconstruction is possible in this case.
Yukito Iba

0.5

0.5
0
0
−0.5

−0.5
1

2

1
3

4
1

2

3

4

2

3

4
1

2

3

4

0.5

1
0.5

0
0
−0.5

−0.5
1

2

1
3

4
1

14

2

3

4

2

3

4
1

2

3

4

Introduction to Our Research

The Estimation of Space- and Time-spectrum in Dispersive Wave Field

Figure 1: Spatial waves of ( A ) not dispersive and( B ) dispersive phase velocity case.

The phase velocity dispersion function, which isimportant in many fields, can be estimated knowing both of the time spectrum and the space
spectrum of the wave, in principle.
The surface wave of the earthquake is a typicalphenomenon that waves have the properties ofdispersion. The relation of wavelength and phasevelocity of surface waves depends on crustal
structure (the relation of depth and physicalproperties) beneath the observation spot.
Therefore, We can estimate crustal structure bymeasuring the time and the spatial spectrum.
Generally, for observation of seismic wave, we
use the seismic array consist of seismometers developed on a region of land surface. In real observation, there is limitation in number of seismometers
to be used, and consequently the resolution of thespatial spectrum is considerably low comparedwith that of time spectrum. Our purpose of thisresearch is to estimate more accurate space
spectrum and phase velocity dispersion function
utilizing abundant time spectrum data.
Ken Ishikawa

Introduction to Our Research

When we observe natural wave motion phenomena, for example sound wave, seismic wave, or
oceanic wave, we collect the variation at fixed
position or the spatial variation at fixed time.
When we observe propagation of the complicatedwaves which consist of a large number of waves
which are differ each other in wavelengths on
the assumption that the wave field is stationary intime and space domain, the characteristics of the
wavefield is represented by “space spectrum”(the relation between power and frequency),
and “time spectrum” (the relation between power
and wavelength or wavenumber).
The relation of three parameters, wavenumber,frequency, and phase velocity decides relation
between time spectrum and space spectrum.
When the waves have property of the dispersion(Fig.1), that is, the phase velocity varies depending
on wavelength, the space spectrum takes different
shape when the waves are not dispersive (Fig.2).
There is a relation between the time spectrum,
the space spectrum and the dispersion function.

Figure 2: ( a ) Phase velocity, ( b ) time spectrum, and ( c ) space spectrum ofdispersive ( red line ) and not dispersive ( blue line ) case.

15

ISM Research Projects

Organizing the International Symposium on Stochastic Models
and Discrete Geometry

Introduction to Our Research

Discrete geometry is successfully applied to the
problems in Crystallography, Chemistry and Physics. Analyses of statistical data on geometricalstructure suggest various problems in statistics.
We have been organizing Symposiums whose
main theme is “stochastic models and discretegeometry” during these ten years. In recent several years, especially, we have invited a number ofdistinguished researchers from Europe and UnitedStates, and the symposium is obtaining an indisputable unique position inside and outside of Japan.
By the occasion of the invitation of foreign researchers, the international cooperative researches
on stochastic models and discrete geometry havebeen substantially performed.
In this fiscal year, the ISM symposium “Stochastic
Models and Discrete Geometry” was held during
February 26 and 28, 2007 with the aids of Prof.
Yoshiaki Itoh and Dr. Teruhisa Sugimoto.
In this symposium, more than 20 lectures are
given, including several lectures of foreign researchers invited through the project funds, and theactive discussions were exchanged.

16

Followings are the partial list of speakers andtheir title of lectures (only those who were invitedby the project fund are listed):
Eiichi Bannai“Cubature formulas in numericalanalysis and tight Euclidean designs”

Tetsuyuki Yukawa “Evolution of simplicial universe”
Hiroshi Maehara “On a special arrangement of spheres”
Makoto Tagami “Introduction of Schuette and van derWaerden’s paper ‘Das Problem derdreizehn Kugeln’ ”
Motoko Kotani “Geometric aspect of random walks
on a crystal lattice”
Michel Deza

“Zigzag and central circuit structureof two-faced plane graphs”

Nikolai Andreev “Disposition of points on the sphere andextremal problems of polynomials”

Masaharu Tanemura

Introduction to Our Research

Developing Statistical Models for Bycatch in Tuna Fisheries
Historically, when people have considered the impactof fisheries on marine resources, their concerns havelargely focused on the population status of the target
species. In the last decade, however, the issue of bycatch
reduction has been getting more attention.

䡵 Bycatch problem in tuna fisheries䡵 Analysis of species associations
As part of efforts to reduce bycatch in this fishery it isimportant to identify associations among catch and bycatch
species. This is a difficult task because some species arecaught only rarely and mostly in small amounts, whereas
other species are caught much more frequently, sometimes
in very large numbers. To this end, we are developing a
new method for measuring association between variables
in multivariate data that is based on a Tweedie distribution.
The family of Tweedie distributions is a very flexible collection of statistical distributions that can accommodate manyof the characteristics of fisheries data. We have applied
this method to data from the purse-seine fishery for theyear 2000. Preliminary results show that the indices ofassociation obtained with this new method are related
to different groupings of catch and bycatch species, andshow clear large-scale spatial patterns. These indices also
show relationships to environmental variables such assea surface temperature, chlorophyll-a density and mixed
layer depth. Information on relationships between indicesof species association and environmental characteristics
will help to guide the development of approaches for
bycatch reduction.

Introduction to Our Research

In the eastern Pacific Ocean, purse-seine nets are used tocatch tunas. Tunas are detected by fishermen in threedifferent ways: in association with floating objects (“floating object” sets), in association with herds of dolphins(“dolphin” sets), and as free-swimming schools visible atthe surface. Incidental mortality of dolphins, sharks, sea
turtles, and other species can occur during fishing operations for tunas. The incidental mortality of dolphins in thisfishery was the first bycatch problem that attracted publicattention. In the 1960’s, hundreds of thousands of dolphins are estimated to have been killed annually incidental
to fishing operations in dolphin sets. With the development of dolphin-release techniques by fishermen, national
legislation and international agreements establishingquotas on incidental mortalities, and the implementationof a seminar program designed to educate fishermen on
methods for avoiding dolphin mortalities, incidental
mortality of dolphins has declined to less than three
thousand animals annually since 1998. Although dolphinsare rarely killed incidental to fishing operations in othertypes of purse-seine sets, large amounts of bycatch ofmany other species can occur in these sets. We are working with Dr. Cleridy Lennert-Cody of the Inter-American
Tropical Tuna Commission to develop new statisticalmethods to better model bycatch, and the complex relationships that exist between bycatch and catch species.

is a reasonable model for these data. We are using thismodel to estimate temporal trends in shark bycatch
rates, an important relative index of population status.

Mihoko Minami

䡵 Analysis of shark bycatch counts

Figure 1: Observed and predicted distributions for shark bycatch counts

Score value

Annually, shark bycatch occurs in more than one thirdof the floating object sets. One notable characteristic ofshark bycatch data is that there are many sets with zero
bycatch, yet sets with large amounts of bycatch can also
occur. We are developing a zero-inflated negative binomial regression model for the shark bycatch data. The
zero-inflated negative binomial regression model assumes
that there are two states: a “complete” state in which bycatch never happens, and an “incomplete” state in which
bycatch might occur. In the incomplete state, bycatchcounts are assumed to follow a negative binomial regression model. We have fit this model to shark bycatch datafrom floating object sets. The distribution of the predictedbycatch was found to be quite
similar to that of the observed
bycatch suggesting that thezero-inflated negative binomial

Figure 2:
Spatial distributions of score values andrelationship between the first score andsea surface temperature

Sea surface temperature ( ˚C )17

Introduction to Our Research
ISM Research Projects

Analytic Center of the System of Linear Matrix Inequalities and Its Applications
There are many problems in machine learning,knowledge discovery and statistical sciences
which reduce to computation of the analytic centerof the system of linear matrix inequalities. For
example, we can estimate high-dimensional normal
distributions by computing analytic centers. New
models are also developed for density estimationand estimation of intensity functions of a nonstationary poisson process by computing the analytic
center, to which MAIC method can be applied
based on the rigorous maximum likelihood estimation.
We developed a polynomial-time primal-dual
interior-point algorithm for computing the analytic
center. Now we study implementation of the algorithm. Though our algorithm is efficient in theory,
there are several issues to be resolved towards apractical fast and robust implementation. This
year, we discussed the model class handled in theimplementation and studied suitable data structures.
Takashi Tsuchiya

Introduction to Our Research

The large-scale problem in computational mathematics has long been regarded intractable if it
is “nonlinear.”Techniques are mainly developed for individual
nonlinear problems and it was difficult to develop
a systematic approach. Recently, remarkableprogress is made on mathematics and algorithms
for treating well-behaved nonlinearlity called“convexity.” In particular, there are several practical polynomial-time algorithms for importantclasses of optimization problems over convex sets.
This enables us to introduce new models in statistical science, machine learning, control etc.
The system of linear “matrix” inequalities treatedin this research is an extension of the system of
linear inequalities. The set of solutions of thesystem of linear matrix inequalities is curved
but is convex. The analytic center of the systemof linear matrix inequalities is defined as thepoint which maximizes the determinant in thesolution set. This point is computed efficiently
with the interior-point algorithms.

X*S
C
0

0
SR

1
SR

C
1

CentralTrajectory

2
SR
OptimalSolution

Neighborhood(Inside theenclosed area)

C
2

0
=0
is greater than zero
above this line.)
The extended central trajectory traced by the algorithm for computing the analytic center and the neighborhood of the trajectory18Research Cooperation

International Cooperation
䡵 Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washigton )July 27, 1988 -Stichting Mathematisch Centrum

The Kingdom of the Netherland ( Amsterdam )May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University

The Republic of Korea ( Seoul )October 17, 2002 -Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany ( Berlin )December 8, 2004 -Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei )June 30, 2005 -The Steklov Mathematical InstituteRussia (Moscow)

August 9, 2005 -Central South University

China ( Changsha )November 18, 2005 -Soongsil University

The Republic of Korea ( Seoul )April 27, 2006 -Department of Statistics, University of Warwick

The United Kingdom ( Coventry )January 16, 2007 -Research Collaboration

Research Cooperation

This academic study program provides researchers from other academic institutes with access to thefacilities of the Institute, and provides opportunities for researchers to conduct theoretical and appliedstudies on statistics.

䡵 Fields of Research Collaboration
A new field classification rule has been introduced in 2007. It has two streams of classification: the ISM
field and the major research field. Applicants can use the table below to find the most appropriate type of
project.
ISM Fields
NumberFieldsNumberFieldsa

Spatial and time series modelingfComputational statisticsbIntelligent information processinggMathematical statisticsc

Graph modelinghLearning and inferencedSurvey researchiComputational mathematicseMultidimensional data analysisj

OthersMajor Research Fields
NumberFieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, low, politics, society, management, official statistics, population, etc.

8

OthersOther research fields

19

Research Cooperation
Research Collaboration
Number of Activities

Previous Categories ( - 2006 )FieldsMajor Research Domains

1. Fundamental theories

Statistical inference theory, applied probability theory, fundamental studies ofdata analysis, history of statistics, statistics education

2. Computation and optimization

Optimization, large linear computation, super high dimensional numericalintegration, importance sampling, random number, neural networks, time series,
inference computation

3. Time series

Modeling, analysis, prediction and control of time series

4. Survey theories

Acquisition and analysis method of statistical data, studies on the theory andmethod for statistical proof

5. Science and engineering

Practical use of statistics for science engineering

6. Space and earth science

Statistical data analysis related to earth, planetary and space sciences

7. Biology and medicine

Statistical studies in biology, medicine, pharmaceutics, agriculture, life science, etc.

8. Humanities and social science

Studies of statistical data in humanities or social science

9. Environment science

Statistical studies on the environment and ecology

10. Others

Other research fields

Theory andMethodology

Application

Research Cooperation

Number of Activities
Fields

Theory andMethodology

Application

2002

2003

2004

2005

2006

1. Fundamental theories

22

18

18

18

19

2. Computation and optimization

8

7

12

13

8

3. Time series

16

13

15

14

12

4. Survey theories

2

3

2

3

3

5. Science and engineering

10

6

10

11

11

6. Space and earth science

8

5

7

11

10

7. Biology and medicine

26

25

24

26

34

8. Humanities and social science

13

10

9

12

12

9. Environment science

6

10

9

11

9

10. Others

3

2

2

5

4

114

99

108

124

122

Total

20

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education andresearch program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science
Accelerator Science

President
School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics
Basic Biology

School of Life Science

Physiological Sciences
Biosystems Science
Photo Science

Graduate School Program

School of Advanced Sciences

Outline of Education and Research
The course includes modeling, forecasting, inference and designing of data-gathering systems in order
to extract information and knowledge from the real world based on empirical data, as well as basic, mathematical and applied education and research related to these fields. The course aims to provide the student with skills that help to contribute to solving important and connected issues and give the ability toperform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information processing.
We also provide education and research on statistical inference based on various models, methods of calculation
for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with the uncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

21

Features of Education and Research The course is the only integrated doctoral program on statistical science in Japan. It has received students from a wide variety of disciplines and has offered education and research on statistical scienceby professors specialized in many different fields, from theory through to practical applications.
 The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.
 The academic publications and books on statistical and mathematical sciences produced are some of
the best in the world.
 The library holds an extensive collection of books and journals, being one of the best in the world.
 In its role as an inter-university research institute, the Institute holds frequent workshops and seminars
by visiting professors and researchers from both Japan and abroad. Students are free to attend andparticipate.
 It is possible to collaborate with researchers from other universities and institutions. It is also possible
for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted
 Requirements to complete the course are as follows:
Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.
 On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.
 The required number of years of study will be flexible if a student demonstrates outstanding research
results.

Graduate School Program

Number of Students ( As of April 1, 2007 ) Doctor’s course three years
Year of enrollment

2002

2003

2004

2005

2006

2007

Number of students

1

2

6 ( 1) 5

3 ( 1)

5* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

 Doctor’s course five years

22Year of enrollment

2006

2007

Number of students

12Graduate School Program

University Background of Students
National and public universities• Hokkaido University • Tohoku University ( 2 ) • Fukushima University • University of Tsukuba ( 5 ) • Saitama University• Ochanomizu University • Hitotsubashi University • Chiba University • The University of Tokyo ( 13 ) • Tokyo Gakugei University• Tokyo University of Agriculture and Technology • Tokyo Institute of Technology ( 2 ) • Tokyo University of Marine Science and Technology• Nagoya University ( 2 ) • Kyoto University ( 2 ) • Osaka University • Okayama University • Shimane University ( 2 ) • Kyushu University ( 2 )• Japan Advanced Institute of Science and Technology, Hokuriku • Osaka City University
Private universities• Keio University ( 4 ) • Chuo University ( 4 ) • Tokyo University of Science ( 4 ) • Toyo University • Nihon University ( 2 ),• Japan Women’s University • Hosei University ( 7 ) • Waseda University ( 5 ) • Kyoto Sangyo University • Okayama University of Science• Kurume University
Foreign universities• Aston University • Center for Analysis and Prediction, China Seismological Bureau• Chinese Academy of Sciences, Institute of Applied Mathematics • Jahangirnagar University ( 2 ) • Northeastern University, China• Ohio University • Stanford University • The Hong Kong University of Science and Technology • Universidade Eatadual of Campians• University of Colorado at Boulder ( 2 ) • University of Dhaka ( 2 ) • University of Hawaii • University of Malaya • University of Rahshahi• University of Science and Technology of China

Degrees AwardedYear

Doctor of Philosophy

Year

Doctor of Philosophy

1993
1994
1995
1996
1997
1998
1999

2
7
6 [1]
3 [1]
1
4 [1]

2000
2001
2002
2003
2004
2005
2006

5
5
4
8 [ 3]
4
4
8 [1]

6

* [ ] Ph.D. on the basis of the dissertation only ( included in the total )Current Position of Alumni ( As of April 1, 2007 )Graduate School Program

National and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine, Professor • University of Tsukuba, Professor ( 2 )• University of Hyogo, Professor • Saitama University, Associate Professor • The University of Electro-Communications, Associate Professor• The University of Tokyo, Associate Professor • Kyushu University, Associate Professor • Kyushu Institute of Technology, Associate Professor• The Institute of Statistical Mathematics, Associate Professor • University of Tsukuba, Lecturer • Hokkaido University, Assistant• Chiba University, Assistant • Tokyo Institute of Technology, Assistant • Hiroshima University, Assistant• Kyushu University, Assistant Professor • University of the Ryukyus, Assistant • The Institute of Statistical Mathematics, Assistant Professor ( 5)
• The University of Tokyo, Project Researcher • Tokyo Institute of Technology, Research Fellow• Nara Institute of Science and Technology, Research Fellow • The Institute of Statistical Mathematics, Project Researcher ( 3)
• Bank of Japan, Project Post • Financial Services Agency, Financial Research and Training Center, Researcher• The Institute of Statistical Mathematics, JST CREST Research Fellow • Railway Technical Research Institute, Senior Researcher• Statistical Information Institute for Consulting and Analysis • Public School, Teacher • The Institute of Statistical Mathematics, Research Fellow ( 2 )Private universities• Sapporo Gakuin University, Professor • Meiji University, Professor • Doshisha University, Professor• Tokyo Health Care University, Associate Professor • Nihon University, Associate Professor • Tokyo University of Information Science, Lecturer• Josai University, Lecturer • Sapporo Gakuin University, Full-Time Lecturer • Tokyo Women’s Medical University, Postdoctoral Fellow
Foreign universities• Asia-Pacific Center for Security Studies Department, Associate Professor • Central South University, Professor• Hong Kong Baptist University, Lecturer • Jahangirnagar University, Professor • Jahangirnagar University, Associate Professor ( 2 )• Massey University, Research Fellow • The University of Warwick, Research Fellow • University of Otaga, Research Fellow• University of Rajshahi, Associate Professor • University of South Carolina, Research Fellow • Victoria University, Senior Lecturer
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory, Research Fellow • NTT Communication Science Laboratories, Research Fellow• Seiwa Kikaku • Toyota Motor Corporation, Higashi-Fuji Technical Center, Research Fellow • NLI Research Institute, Chief Researcher• Sankyo Co., LTD. • Mizuho Trust and Banking, Senior Researcher ( 2 ) • JP Morgan Trust Bank Limited, Vice President ( Hosei University, Part-Time Lecturer )• ATR Computational Neuroscience Laboratories, Research Fellow • Schlumberger Limited• Macquarie Securities, Japan, Quantitative Analyst • Non-Life Insurance Rating Organization of Japan, Staff Member

23Outreach Activities

Tutorial Courses
䡵 History
In 1944, the year that the Institute of Statistical Mathematics was founded, an education program for employedpeople was initiated at the 1st Period Numeric Computation Training Center of the Science ResearchTechnical Assistant Training Center of the Ministry of Education. In 1947, the affiliated Statistical Technician
Training Center was opened as an educational organization for statistical technicians and instructors, inorder to improve the levels of staff training within the statistical organizations of the government and to
supply extra statisticians.
As social needs have changed, the purpose of the education program has gradually shifted away fromthe initial aim of supplying well qualified statistical technicians for the government, towards statisticaleducation for employed people. Tutorial courses were therefore initiated. Later statistical methods became
more popular and were applied to a wider range of fields. Consequently, more extensive and sophisticatedstatistical education was required. To meet this demand, tutorial courses were expanded to cover a wider
range of statistical topics. From 1965 to 1985, six to eight courses were held annually, not only in Tokyo,
but also in provincial cities such as Osaka, Okayama and Fukuoka.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation,and the affiliated Statistical Technician Training Center was abolished. However, in response to consistentpublic demand for tutorial courses, the Center for Engineering and Technical Support, together withother departments ran three to four courses annually. In 2005, the number of courses rose to 13. In 2006,
the number of courses was 15.

䡵 Courses
The total number of courses held from 1969 to March, 2007 was 220, with a total of 17,133 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past 5 years:
Year

Category

Title

Month

2002

Basic course

Introduction to Statistics

July

95

Standard course A

Introduction to Monte Carlo Filter and its Application

October

71

Standard course B

Frontiers in Statistics, Neural-net, Learning — Part 2

November64

Advanced course

How Can New Technologies of Computer Science Promote Statistical Science ?

March

38

Basic course

Introduction to Statistics

July

91

Standard course A

Introduction to Sampling Methods and Sample Surveys

July

93

Standard course B

Textual Data Mining and its Applications

November72

Advanced course

Markov Chain Monte Carlo — Algorithms and Applications to Statistical Science —February

82

Basic course

Elementary Statistics

October

93Standard course

Recent Topics from Machine Learning

November

91

2003

2004

2005

Outreach Activities

2006

24

Number of participants

Advanced course

Speech Processing — For Development of New Statistical Methods —December

59

Basic course

Introduction to Statistics

September

85

Advanced course

A Junction of Informatics — Chordal Graph and its Applications —September

13Standard course

Analysis of Qualitative Data by Quantification Methods

October50

Basic course

Nonlinear Time Series Analysis of Financial Data

October52

Basic course

Introductory Data Analysis with R

November98

Advanced course

Data Processing and LSI Design for Information andTelecommunications with the Latest Technologies

November - December

5

Basic course

Introduction to Sampling Methods and Sampling Surveys

December75

Basic course

Introduction to Reliability Theory and Survival Data Analysis with R

December

39Standard course

Theory and Practice Inferring Molecular Phylogenies

January

72

Basic course

Introduction to Probabilistic Evaluation of Risk

January

45

Standard course

Non-Poisson Regression Models for Count Data

February

39

Advanced course

Packing and Random Packing

February

7

Basic course

Introduction to Time Series Analysis

March

72

Year

2007

Category

Title

Month

Number of participantsStandard course

A Course on Time Series Analysis for Economics and Finance

June

40

Standard course

Advances in Kernel Methods: SVM, Nonlinear Data Analysis, and Structured Data

July

73

Basic course

Basic Medical Statistics Using R

July

20

Basic course

Introduction to Statistics

July

69

Standard course

Lectures on Information Theory and Mobile Telecommunication Technologies— Systems and Hardwares for Large-Scale Data Processing —August-September

13

Basic course

International Standardization of Statistical Methods
September— Precision and Trueness of Measurement Methods and Results — Capability of Detection

22

Advanced course

A New Trend of Adaptive and Learning Control Theory

September

14

Basic course

A Game Theoretic Approach to Mathematical FinanceNovember21

Basic course

Introduction to Quantitative Methods for Social Sciences

November - January 43

Standard course

Statistical Pattern Recognition

NovemberBasic course

Introduction to Statistical Data Analysis

November - March 13

Standard course

Statistical Mathematics of Rock-Scissors-Paper Game

November - December

7

Standard course

An Introduction to Statistical Analysis Based on the Theory of Martingales

December38

Basic course

Introduction to Risk Analysis with R— Application of Tree-based and Nonparametric Modelling —January

49

Basic course

Introduction to Survey Data Analysis Using R

February

40

65

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

ISM Seminar
The Institute holds a 1-hour seminar every Wednesday starting at 1:30 PM, in the seminar room on thesecond floor of the annex building. Teachers of the Institute and other Japanese or foreign researchers
will be invited to speak. This seminar may be held in association with other remote universities or academicorganizations via a space collaboration system (SCS). On that occasion, it will be held in the lecture hallon the second floor of the main building. The seminar is open to everyone. For details of the seminarschedule and further information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

Special Lecture

Outreach Activities

We hold a special lecture during Education and Culture Week every year (November 1 through 7), tointroduce an aspect of the Institute’s activities and to promote statistical science. Several lecturers will be
invited to talk about particular topics, and they will clearly explain up-to-date topics involving statisticalscience. In 2005 this special lecture was held at the Hitotsubashi Memorial Hall to mark the opening of the
Risk Analysis Research Center. The topic of 2006 was “Discovery and analysis of stochastic models”. The
lecture is open to everyone. For details of the schedule of the special lecture program, please visit thewebsite of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/25Outreach Activities

Statistical Consultation Service
The Institute provides a consultation service on statistical science for the general public and researchers
as a means of actively sharing the benefits of our research with society. This service operates throughout
the year. Please contact the Center for Engineering and Technical Support. The consultation service covers
a variety of topic ranging from the basics of statistics to more specialized issues. Half of the consultants arefrom the private sector, and the rest are staff from public organizations, university teachers, and students.
Each teacher at the Institute directly takes charge of about 20 specialized cases annually, about 40% of
which are publicized at academic conferences, as being of benefit to society.

Annual Research Report Meeting for 2006
The last Annual Research Report Meeting of the Institute was held on March 15 and 16, 2007. This annual
meeting is an opportunity for the Institute's teachers and visiting teachers to present the results of theirstudies from the previous year. The meeting has been held every year since the foundation of the Institutein 1944. In the early years, as there were few staff members and participants, they were able to have enthusiastic discussions all day long. However, as the number of the staff members has increased, the meeting
is now held over two days and each presenter is given 15 minutes to report on the results of his/her own
research. A wide variety of topics are covered, ranging from statistical contributions to issues facing today’ssociety to more fundamental studies.
This year’s meeting started with the opening address by Junji Nakano, and the greetings from Genshiro
Kitagawa. A total of 47 research education staff, 7 visiting teachers then gave their presentations. In addition
poster session was held by 19 research fellows and a research education staff from this year. From 2004, wehave begun to compile and distribute proceedings in advance. We also host visitors from other organizations.
The program for this meeting is available from the website.
http://www.ism.ac.jp/

Outreach Activities

Poster session

26

Finance and Buildings
Finance and Buildings

Administration Subsidy and Others ( 2006 )Type

Personnel expenses

Non-personnel expenses

Total

981,421793,042

1,774,463

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2006 )Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

4

5

3

12

Income16,290

3,313

3,750

23,353
Unit: ¥1,000

Grant-in-Aid for Scientific Research ( 2006 )Research Category

Items

Amount Granted

Scientific research in priority areas

1

1,500

Scientific research ( A )340,560Scientific research ( B )618,200Scientific research ( C )13

13,100Exploratory research

3

4,300

Grant-in-aid for young scientists

8

7,200

Grant-in-aid for special purposes

1

1,100

Grant-in-aid for JSPS fellows

3

3,200

38

89,160Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2007 )Site Area

5,033m 2

Area for Buildings (total)

6,305m 2

Name of Building

FloorsTotal Area

OfficeR34,855m2

Information Statistics Research Laboratory

R3

1,024m2

Computation Laboratory

R2

368m2

Gym, etc.

S1

58m2

27

Facilities and Equipment

Computation Resources ( As of April 1, 2007 )Facilities and Equipment

Since January 2004, “Supercomputer System for Statistical Science” has been in operation and has analyzed
a large volume of statistical data. The main components of this system comprise a SGI Altix3700 Super
Cluster (a parallel computer system with 256 Itanium2 processors and about 2 TB main memory), a NEC
SX-6 (a vector-type computer system with 12 vector processors and 128 GB main memory), and a HITACHI
SR11000 (a parallel computer sub-system with 64 Power4+ processors and 128 GB main memory). In January
2006, “System for Computational Statistics” was renewed. The main components of this system consistof a HP XC4000 Cluster system with 256 Opteron processors for computing node and 640 GB main memory,a SGI Prism visualization system with 16 Itanium2 processors and 32 GB main memory, and a large display
system (Multi Opt View).
In December 1998, an Ethernet network using 1000Base-SX as a main trunk and 100Base-TX as branches
was laid out as a Local Area Network. Workstations, personal computers in researchers’ offices,“Supercomputer System for Statistical Science”, and “System for Computational Statistics” were all connectedto the network. This Local Area Network enables distributed processing
and computation resources and statistical data to be used effectively.
The development of programs, which run on “Supercomputer System
for Statistical Science” and on “System for Computational Statistics” byoperating from workstations and personal computers in researchers’
offices, is also underway. To encourage joint research with researchers
both in Japan and abroad, as well as the exchange of e-mails, the network is connected to the internet through SINET. The connection
speed of 1.5 Mbps during FY 1999 has risen to 100 Mbps since July
2002. Since April 2007, the network has also been connected to the
SINET3 with 2.4 Gbps bandwidth. Some machines are able to communicate at a rate of 1 Gbps. Through terminal servers, the network isalso accessible from a public line. In addition, comprehensive network
security measures have been implemented such as the adoption ofanti-virus software as well as a network monitoring system.
SGI Altix3700 Super Cluster, a parallel computer

Library and Materials ( As of April 1, 2007 )We have a large number of major Japanese/foreign journals covering a wide variety of fields including
statistics, mathematics, computer science and informatics. In addition, we also have a large library consisting of books on humanities, social science, biology, medical science, science and engineering.
Besides contributed to Japanese and foreign publications, we also have a collection of journals that
we publish ourselves: Annals of the Institute of Statistical Mathematics (English; Springer), Proceedings ofthe Institute of Statistical Mathematics (Japanese), Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science Monographs, Cooperative Research Reports (forcollaborative research projects), Research Memorandum, ISM Reports
JapaneseJapanese
Foreignon Statistical Computing, and ISM
books
periodicals
periodicals
Report on Research and Education.
14,460All materials are properly cata27%
Books
Periodicals
1,210
967
logued and can be searched from the
56%
44%
2,177
52,753
web in order to meet the needs of researchers working in a wide of fields.
We also accept photocopy requests.
Foreign books
38,29373%

28

Organization

Organization Diagram ( As of April 1, 2007 )Council
Cooperative
Research
Committee

䡵 Spatial and Time Series Modeling Group䡵 Intelligent Information Processing Group䡵 Graph Modeling Group

Department of Data Science䡵 Survey Research Group䡵 Multidimensional Data Analysis Group䡵 Computational Statistics Group

Department of Mathematical
Analysis and Statistical Inference䡵 Mathematical Statistics Group䡵 Learning and Inference Group䡵 Computational Mathematics Group

Prediction and Knowledge Discovery
Research Center ( PKDRC )䡵 Molecular Evolution Research Group䡵 Data Assimilation Research Group䡵 Statistical Seismology Research Group䡵 Statistical Genome Diversity Research Group

Risk Analysis Research Center( RARC )䡵 Food and Drug Safety Research Group䡵 Environmental Risk Research Group䡵 Financial Risk and Insurance Research Group

Center for Engineering andTechnical Support

䡵 Computing Facility Unit䡵 Networking Facility Unit
䡵 Education and Library Unit
䡵 Public Outreach Unit

DirectorGeneral
Vice Director-General
Administration Planningand Coordination Unit

Administration
OfficeGeneralAdministration
Section䡵 General Affairs Unit
䡵 Personnel Unit
䡵 Research Cooperation Unit

Accounting
Section䡵 General Affairs Unit
䡵 Auditing Unit䡵 Supplies Unit
䡵 Property Maintenance Unit

Organization

Department of Statistical ModelingNumber of Staff ( As of July 1, 2007 )Type

Director-General

DirectorGeneral

Professor

Associate
Professor

Assistant
Professor

Clerk

Technical
Staff

1

Total

1

Department of Statistical Modeling

5

6

6

17

Department of Data Science

6

7

5

18

Department of Mathematical Analysis
and Statistical Inference

4

5

3

12

Center for Engineeringand Technical Support
Administration Office
Total

1

15

18

14

10 ( 1)10 ( 1)

13

2

15

13

12 ( 1)73 ( 1)() Staff of reemployment

29

Staff ( As of July 1, 2007 )Director-General

Genshiro KITAGAWA

Vice Director-General

Masaharu TANEMURA

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Tomoyuki HIGUCHI

Department of Statistical Modeling䡵 Spatial and Time Series Modeling Group
Prof.
Prof.
Prof.
Prof.

Tohru OZAKI
Masaharu TANEMURA
Yosihiko OGATA
Tomoyuki HIGUCHI

Makio ISHIGURO

Director

Juan Carlos Jimenez-Sobrino
Nikolai Petrovich Dolbilin
Assoc. Prof. Yoshinori KAWASAKI
Visiting Prof.

Assist. Prof.

Visiting Prof.

Assist. Prof.
Assist. Prof.
Assist. Prof.

Kenichiro SHIMATANI
Genta UENO
Ryo YOSHIDA
Jiancang ZHUANG

䡵 Intelligent Information Processing Group

Organization

Makio ISHIGURO
Toshio IRINO
Visiting Prof. Jean-Philippe Vert

Yukito IBA
Yumi TAKIZAWA
Assoc. Prof. Tomoko MATSUIProf.

Assoc. Prof.

Assoc. Prof.

Visiting Prof.

Assoc. Prof.

Visiting Assoc. Prof.
Assist. Prof.

Kenji FUKUMIZU
Arnaud Doucet
Hiroshi SOMEYA䡵 Graph Modeling Group
Assoc. Prof.

Jun ADACHIAssist. Prof.

Ying CAO

Department of Data Science
Director䡵 Survey Research Group
Prof.
Prof.

Takashi NAKAMURA
Ryozo YOSHINO

Visiting Prof.
Assoc. Prof.

Takashi NAKAMURA

Nicolaos Emmanuel SYNODINOSTadahiko MAEDA

Assoc. Prof.

Nobuhisa KASHIWAGI
Satoshi YAMASHITA
Sumie UEDA

Assist. Prof.

Assist. Prof.

Takahiro TSUCHIYA
Wataru MATSUMOTO

䡵 Multidimensional Data Analysis Group

Yasumasa BABA
Toshiharu FUJITA
Visiting Prof. Masahiro MIZUTAProf.

Assoc. Prof.

Prof.

Assoc. Prof.
Assist. Prof.

Assist. Prof.

Toshio OHNISHI
Toshihiko KAWAMURA

䡵 Computational Statistics Group

Yoshiyasu TAMURA
Junji NAKANO
Visiting Prof. Yoshinari FUKUIVisiting Prof. Makoto TAIJI
Visiting Prof. Makoto MATSUMOTO

Michiko WATANABE
Shigeji FUJITA
Assoc. Prof. Naomasa MARUYAMA
Assoc. Prof. Koji KANEFUJI
Assoc. Prof. Seisho SATO

Prof.

Visiting Prof.

Visiting Assoc. Prof.

Prof.

Visiting Prof.

Visiting Assoc. Prof.
Assist. Prof.

Tohru ONODERA
Takeshi KOSHIBA
Nobuo SHIMIZU

Department of Mathematical Analysis and Statistical Inference䡵 Mathematical Statistics Group
Prof.
Prof.

Katuomi HIRANO
Satoshi KURIKI

Director

Katuomi HIRANO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Yoichi NISHIYAMA

Assoc. Prof.

Mihoko MINAMI
Shiro IKEDA

Assoc. Prof.

Assoc. Prof.

Assist. Prof.

Hironori FUJISAWA
Tadayoshi FUSHIKI

Assoc. Prof.

Satoshi ITO

Assoc. Prof.

Yoshihiko MIYASATO

䡵 Learning and Inference GroupProf.
Visiting Prof.

Shinto EGUCHI
Kunio SHIMIZU

䡵 Computational Mathematics GroupProf.
Visiting Prof.

30

Takashi TSUCHIYA
Leonid Faybusovich

Organization

Prediction and Knowledge Discovery Research Center ( PKDRC )Director䡵 Molecular Evolution Research Group
Assoc. Prof.

Jun ADACHIAssist. Prof.

Shinto EGUCHI
Ying CAO

Research Fellow upon JSPS Program Takeshi SASAKI
Project Researcher

Atsushi MATSUI䡵 Data Assimilation Research GroupProf.
Visiting Prof.

Tomoyuki HIGUCHI
Takashi WASHIO

Assist. Prof.
Assist. Prof.
Project Researcher

Genta UENO
Ryo YOSHIDA
Alexandre TERMIER

Researcher, JST CREST

Jiancang ZHUANG
Takaki IWATA
Ushio TANAKA

Assoc. Prof.

Mihoko MINAMI
Shiro IKEDA
Hironori FUJISAWA
Tadayoshi FUSHIKI

Project Researcher

Researcher, JST CREST
Researcher, JST CREST

Shinya NAKANO
Kazuyuki NAKAMURA
Daisuke INAZU

䡵 Statistical Seismology Research Group

Yosihiko OGATA
Shinji TOHDA
Visiting Assoc. Prof. Jeffrey J. McGuire
Prof.

Assist. Prof.

Visiting Prof.

Project Researcher
Project Researcher

Yasuaki MURATA

Institute of Geoscience,
Geological Survey of Japan, AIST

䡵 Statistical Genome Diversity Research Group

Shinto EGUCHI
Satoshi KURIKI
Visiting Prof. Hirofumi WAKAKI
Visiting Prof. Su-Yun Huang
Prof.

Assoc. Prof.

Prof.

Assoc. Prof.
Assoc. Prof.
Assist. Prof.

Project Researcher

Takayuki SAKAGUCHI
Md Nurul Haque Mollah

Organization

Risk Analysis Research Center ( RARC )Toshiharu FUJITA
Hiroe TSUBAKI
Visiting Prof. Manabu IWASAKI

Hiroe TSUBAKI

Director䡵 Food and Drug Safety Research GroupProf.

Visiting Prof.

Toshiya SATO
Kunihiko HAYASHI
Visiting Assoc. Prof. Satoshi AOKIVisiting Assoc. Prof.

Visiting Prof.

Visiting Prof.

Visiting Assoc. Prof.

Nobuhisa KASHIWAGI
Koji KANEFUJI
Visiting Assoc. Prof. Hideshige TAKADAAssist. Prof.

Toshimitsu HAMASAKI
Yoshimitsu HIEJIMA
Takaaki SHIMURA

䡵 Environmental Risk Research Group

Yukio MATSUMOTO
Kazuo YAMAMOTO
Visiting Prof. Yoshiro ONO
Visiting Prof.

Assoc. Prof.

Visiting Assoc. Prof.

Visiting Prof.

Assoc. Prof.

Visiting Assoc. Prof.
Assist. Prof.
Project Researcher

Hirokazu TAKANASHI
Tomohiro TASAKI
Toshihiko KAWAMURA
Mitsuhiro TOMOSADA䡵 Financial Risk and Insurance Research Group
Visiting Prof.
Visiting Prof.
Visiting Prof.
Assoc. Prof.

Tata Subba Rao
Naoto KUNITOMO
Hiroshi TSUDA
Satoshi YAMASHITA

Seisho SATO
Yoshinori KAWASAKI
Visiting Assoc. Prof. Toshinao YOSHIBA
Project Researcher Yoko TANOKURA
Assoc. Prof.

Project Researcher

Assoc. Prof.

Project Researcher
Project Researcher

Masakazu ANDO
Masayuki KUMON
Ken-ichi KAWAI

Postdoctoral Researchers
Project Professor
Project Researcher

Atsushi FUKASAWA
Masahiro OKABE

Project Researcher

Satoko KAWARASAKI

Research Fellow upon JSPS Program Shohei SHIMIZU

Project Researcher
Project Researcher

Teruhisa SUGIMOTO
Yoshiyuki TSUDA

Center for Engineering and Technical Support
Director

Junji NAKANOHead of Computing Facility Unit
Head of Networking Facility Unit

Vice Director

Satoshi YAMASHITA

Saeko TANAKA
Kazuhiro NAKAMURA

Deputy Manager ( Concurrent ) Setsuko TERAOHead of Education and Library Unit
Head of Public Outreach Unit

Setsuko TERAO
Yuriko WATANABE

Administration Office䡵 General Administration Section
Manager of General Administration Section

General Manager

Toshifumi HAGIWARA

Yoshifumi KUROKAWA

Chief of Personnel Unit

Deputy Manager of General Administration Section Shigeyuki FUJII
Chief of General Affairs Unit

Fumio SUTO

Masami SAKAO

Chief of Research Cooperation Unit (Concurrent) Shigeyuki FUJII

䡵 Accounting Section
Manager of Accounting Section
Deputy Manager of Accounting Section
Chief of General Affairs Unit

Toyokichi KITAHARA
Tetsuya KUMAZAWA
Minoru HAGIWARA

Chief of Auditing Unit (Concurrent)
Chief of Supplies Unit
Chief of Property Maintenance Unit

Minoru HAGIWARA
Yoshiyuki SAKATA
Hirotomo SHIMIZU

31

Organization

Council of The Institute of Statistical Mathematics ( As of April 1, 2007 )Organization

Yutaka KANO

Professor, Graduate School of Engineering Science, Osaka University

Toshinari KAMAKURA

Professor, Faculty of Science and Engineering, Chuo University

Naoto KUNITOMO

Professor, Graduate School of Economics, the University of Tokyo

Sadanori KONISHI

Professor, Graduate School of Mathematics, Kyushu University

Yoshiharu SATO

Professor, Graduate School of Information Science and Technology, Hokkaido University

Makoto TAIJI

Team Leader, Genomic Sciences Center, RIKEN

Katsuto TANAKA

Professor, Graduate School of Economics, Hitotsubashi University

Fumi HAYASHI

Professor, Department of Human Sciences, Toyo Eiwa University

Yoshihiko MIZUMOTO

Chief of Astronomy Data Center, National Astronomical Observatory of Japan, National Institutes of Natural Sciences

Satoru MIYANO

Professor, Human Genome Center, the Institute of Medical Science, the University of Tokyo

Masaharu TANEMURA

Professor ( Vice Director-general, ISM ( General Affairs ) )Yoshiyasu TAMURA

Professor ( Vice Director-general, ISM ( Assessment ) )Tomoyuki HIGUCHI

Professor ( Vice Director-general, ISM ( Research Planning ) )Makio ISHIGURO

Professor ( Director of Department of Statistical Modeling, ISM )Takashi NAKAMURA

Professor ( Director of Department of Data Science, ISM )Katsuomi HIRANO

Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM )Shinto EGUCHI

Professor ( Director of Prediction and Knowledge Discovery Research Center, ISM )Junji NAKANOProfessor ( Center for Engineering and Technical Support, ISM )Yosihiko OGATA

Professor ( Department of Statistical Modeling, ISM )Satoshi KURIKI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Takashi TSUCHIYA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Cooperative Research Committee ( As of June 1, 2007 )Manabu IWASAKI

Professor, Faculty of Science and Technology, Seikei University

Hidetoshi KONNO

Professor, Graduate School of System and Information Engineering, University of Tsukuba

Masahiro MIZUTA

Professor, Information Initiative Center, Hokkaido University

Michiko WATANABE

Professor, Faculty of Economics, Toyo University

Makio ISHIGURO

Professor ( Department of Statistical Modeling, ISM )Takashi NAKAMURA

Professor ( Department of Data Science, ISM )Shinto EGUCHI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Takashi TSUCHIYA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Professor Emeritus ( As of April 1, 2007 )32

Hirojiro AOYAMA

Hirotugu AKAIKE

Masakatsu MURAKAMI

Yoshiyuki SAKAMOTO

Kameo MATUSITA

Giitiro SUZUKI

Kunio TANABE

Takemi YANAGIMOTO

Sigeki NISIHIRA

Ryoichi SHIMIZU

Tadashi MATSUNAWA

Yoshiaki ITOH

Tatsuzo SUZUKI

Noboru OHSUMI

Masami HASEGAWA

History

June, 1944



Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed toprovide supervision for studies looking into the mathematical principles of probability and their application, and was also intended to facilitate, unify and promote the publication of research results.

April, 1947

The affiliated statistical specialists’ school was opened.

May, 1947

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept.
(statistical theories for the natural sciences ), and the 3rd Research Dept. ( statistical theories for the social
sciences ).

June, 1949The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

September, 1955 

Reorganized into the 1st Research Dept. (fundamental theories ), the 2nd Research Dept. ( natural andsocial science theories ), and the 3rd Research Dept. ( operations, research, statistical analysis theories ).
The laboratory system, comprising 9 laboratories and the research guidance promotion room, wasadopted.

October, 1969

A new office building was constructed.

April, 1971

The 4th Research Dept. ( informatics theories ) was instituted.

April, 1973

The 5th Research Dept. ( prediction and control theories ) was instituted.

October, 1975

The 6th Research Dept. ( statistical theories of human behavior ) was instituted.

November, 1979 

The Information Research Building was constructed.



Reorganized as an Inter-University Research Institute owing to the revision of the Order for the Enforcement of the National school Establishment Law. The revised law required that the Institute would, as
an National Inter-University Research Institute, 1 ) conduct studies on statistical mathematics and its
application, 2 ) provide opportunities for university teachers or other researchers majoring in this field
to utilize the facility, and 3 ) contribute to the development of academic studies in universities.
At the same time, the 6 research departments were reorganized into 4 research departments ( Fundamental Statistical Theory, Statistical Methodology, Prediction and Control, and Interdisciplinary Statistics ).
The Statistical Data Analysis Center and the Statistical Education and Information Center were instituted,
and the affiliated Statistical Technician Training Center was abolished.

October, 1988

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies (SOKENDAI).

June, 1999

The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

April, 1993

The Planning Coordination Chief System was instituted.

April, 1997The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

September, 2003 

History

April, 1985

The Prediction and Knowledge Discovery Research Center was instituted in the affiliated facility.

April, 2004

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organization of Information and Systems of the Inter-University Research Institute based on the National
University Corporation Law. The Planning Coordination Chief System was abolished and the position ofVice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathematical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Scienceand the School of Multidisciplinary Sciences were instituted.

April, 2005

The research organization was reorganized into three research departments (the Department of Statistical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical
Inference). The affiliated Center for Development of Statistical Computing, the Center for Information on
Statistical Sciences, and the Engineering and Technical Services Section were integrated into the Center
for Engineering and Technical Support. The affiliated facilities were reorganized as research departments,
and the Risk Analysis Research Center was instituted.

33

Tokyo Metro
Hibiya Line

Hiroo Sta.
Exit 2

Exit 1

Azabu Ground

Nanbuzaka

Embassyof Finland

National Azabu
Supermarket
Bank ofTokyo-Mitsubishi UFJ

Embassyof Germany

Gaien

The Institute of
Statistical Mathematics

oori

Nishi Dto Ebisu

Arisugawanomiya
Park

Tengen-ji Temple
Embassy of France
Me

iji D

Access to the ISM

Tengen-ji Exitoori

Metropolitan Highway
Meguro Line

Tokyo Metro Hibiya Line, Hiroo Sta.
About 7 min on footTokyo Metro Namboku Line, Azabu-jubanSta.
Toei Oedo Line, Azabu-juban
Sta.
About 20 min on footInter-University Research Institute Corporation

Research Organization of Information and Systems

The Institute of Statistical Mathematics
4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan
Tel: +81-(0 ) 3-3446 -1501 (Reception) Fax: +81-(0)3-3443-3552 (General Affairs)
http://www.ism.ac.jp/

The Institute of Statistical Mathematics

to Roppongi